RINet: synthetic data training for indirect estimation of clinical reference distributions.

ABSTRACT:

Background

Indirect methods for estimating clinical reference intervals (RIs) use statistical analysis to identify non-pathological sub-distributions within large datasets acquired from routine clinical testing. This approach has the potential to accelerate the estimation of precise RIs, accounting for influential variables such as age, gender, and ethnicity. Most existing methods are based on traditional statistics and hand-crafted algorithms. The investigation of supervised learning, which often outperforms traditional approaches, has been impeded by the limitations of real-world data. However, previous studies have widely used synthetic data for evaluating and benchmarking indirect methods due several advantages over real-world data, including greater control, variability, acces

SUBMITTER: LeBien J 

PROVIDER: S-EPMC12923325 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature